SMNet: Synchronous Multi-Scale Low Light Enhancement Network With Local and Global Concern
文献类型：期刊论文
| 作者 | Lin, Shideng1,2 ; Tang, Fan3; Dong, Weiming1,2; Pan, Xingjia4; Xu, Changsheng1,2
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| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
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| 出版日期 | 2023 |
| 卷号 | 25页码:9506-9517 |
| ISSN号 | 1520-9210 |
| 关键词 | Low-light image enhancement multi-scale feature learning deep-learning |
| DOI | 10.1109/TMM.2023.3254141 |
| 通讯作者 | Tang, Fan(tangfan@ict.ac.cn) |
| 英文摘要 | Limited by objectively poor lighting conditions and hardware devices, low-light images with low visual quality and low visibility are inevitable in the real world. Accurate local details and reasonable global information play their essential and distinct roles in low-light image enhancement: local details contribute to fine textures, while global information is critical for a proper understanding of the global brightness level. In this article, we focus on integrating local and global aspects to achieve high-quality low-light image enhancement by proposing the synchronous multi-scale low-light enhancement network (SMNet). A synchronous multi-scale representation learning structure and a global feature recalibration module are adopted in SMNet. Different from the traditional multi-scale feature learning architecture, SMNet carries out the multi-scale representation learning in a synchronous way: we first calculate the rough contextual representations in a top-down manner and then learn multi-scale representations in a bottom-up way to generate representations with rich local details. To acquire global brightness information, a global feature recalibration module (GFRM) is applied after the synchronous multi-scale representations to perceive and exploit proper global information by global pooling and projection to recalibrate channel weights globally. The synchronous multi-scale representation and GFRM compose the basic local-and-global block. Experimental results on mainstream real-world dataset LOL and synthetic dataset MIT-Adobe FiveK show that the proposed SMNet not only leads the way on objective metrics (0.41/2.31 improvement of PSNR on two datasets) but is also superior in subjective comparisons compared with typical SoTA methods. |
| WOS关键词 | ADAPTIVE HISTOGRAM EQUALIZATION ; IMAGE QUALITY ASSESSMENT ; ALGORITHM |
| 资助项目 | Beijing Natural Science Foundation |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001133324200019 |
| 资助机构 | Beijing Natural Science Foundation |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54859]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Tang, Fan |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 2.Chinese Acad Sci, Inst Automat, NLPR, Beijing 101408, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Tencent, Youtu Lab, Shanghai 200001, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lin, Shideng,Tang, Fan,Dong, Weiming,et al. SMNet: Synchronous Multi-Scale Low Light Enhancement Network With Local and Global Concern[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2023,25:9506-9517. |
| APA | Lin, Shideng,Tang, Fan,Dong, Weiming,Pan, Xingjia,&Xu, Changsheng.(2023).SMNet: Synchronous Multi-Scale Low Light Enhancement Network With Local and Global Concern.IEEE TRANSACTIONS ON MULTIMEDIA,25,9506-9517. |
| MLA | Lin, Shideng,et al."SMNet: Synchronous Multi-Scale Low Light Enhancement Network With Local and Global Concern".IEEE TRANSACTIONS ON MULTIMEDIA 25(2023):9506-9517. |
入库方式： OAI收割
来源：自动化研究所
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